Requirements for computational rule checking of requests for proposals (RFPs) for building designs in South Korea
Introduction
A request for proposal (RFP) lists the main function, form, usability, and other requirements of a building. A designer then interprets the RFP into a physical form based on his experience and knowledge [1]. During this design phase, the RFP acts as a design guide throughout all design phases and establishes criteria for design assessment [2]. However, RFP-compliance checking is cognitively challenging for designers for several reasons. First, a building is composed of many pieces of elements, which are geometrically complex and interwoven [3], so manual design checking is error-prone and the results are unreliable [2]. Second, designers may misinterpret design requirements in an RFP due to the ambiguous nature of natural language or through human error [4], [5]. Third, RFP-based design checking is very time consuming and labor intensive. According to a survey by McGraw Hill Construction Research and Analytics [6], nearly half of architects and owners spend more than 26 h on code checking on a typical project. Our own early survey shows that architects check their design against an RFP far more frequently than against building codes and other types of design references and they consider an RFP more important than building codes and regulations as a design reference, as shown in Fig. 1 [7]. Fourth, manual checking results in redundant data input, especially in a collaborative design environment, due to the difficulties in data and rule sharing [8].
These problems of complexity, ambiguity, inefficiency, and redundancy can be dramatically reduced by automating the design checking process. An example can be found from medical informatics, where physicians often make mistakes of omitting certain tests or treatments. These errors were reduced by developing an automated reminder for certain treatments and tests based on medical practice guidelines [9].
Interest has been increasing in automated design checking to improve the efficiency of labor and safety at construction sites [10], [11]. The advent of advanced data-rich computer aided design, referred to as building information modeling (BIM), has enabled automate checking of building codes and spatial requirements for notable examples such as the courthouse design guidelines of the U.S. General Services Administration (GSA), the International Building Code of International Code Council (ICC), and the Americans with Disabilities Act (ADA) standard for accessible design [2], [12], [13], [14]. Although code-compliance checking has been the object of many studies for a long time [15], RFP-compliance checking has not been a major focus. Previous research topics have perhaps leaned towards code-checking rather than RFP-checking because building codes are the minimum legal requirements and strictly regulate design and construction submittals for permits. However, building codes do not describe an owner’s specific requirements; thus, improvements in the quality of a building and owner satisfaction require an understanding and proper reflection of the owner’s requirements in an RFP – one without errors or omissions on the design, especially in a competitive bid.
The aim of this paper is to specify the computational requirements for developing computer-interpretable rules for checking the compliance of a building design in a request for proposal (RFP), especially in the building information modeling (BIM) environment. The paper is organized into eight sections. Following this introduction, the next section describes the research scope and method. The third section reviews existing work in the field of automated rule-based design checking. The fourth section briefly introduces the concept of context-free grammar (CFG) and describes how RFP sentences are parsed into a computer-interpretable form using the CFG approach. The fifth section reports the results of RFP sentence analysis and the objects and methods derived from RFPs. The sixth section validates the derived objects and methods using three RFP cases. The seventh section demonstrates the applicability of design rules created using the objects and methods derived in this study using a newly developed BIM model checker and a translator. Finally, the conclusion section discusses the contributions, limitations, and the future direction of this work.
Section snippets
Research scope and method
RFPs may differ by building type, year, regions, and building size [2], [8], [15]. If a building is small or simple, the RFP may not contain many requirements. Hospitals may have different requirements from office buildings, but even if the building types are the same, requirements in the 1980s may differ from those in the 2000s. Requirements for a residential complex in South Korea may also differ from those for similar complexes in the US.
This study limits its scope to the RFPs published for
Previous studies
In order to reduce design errors, a considerable number of studies have focused on the causes of design errors and effective methods to prevent them [21], [22], [23]. The theoretical background of error management studies is often grounded in quality control theories in manufacturing such as ‘six sigma’ [24]. The quality control theories stress the importance of understanding and analyzing customers’ needs and satisfaction in reducing errors and achieving the target quality of outcomes [25],
Analysis of RFPs
The RFPs collected and set up to develop methods were for building projects over USD five million within five years. A total of 27 RFPs were selected and they were composed of six types of facilities. From these RFPs, 1331 computer-interpretable sentences were collected from 9833 sentences written in natural languages and each was made into a single sentence that has a verb. We parsed the computer-interpretable sentence to develop the method and object. This section describes the RFP analysis
Analysis results
From 1331 computer-interpretable sentences and 7040 table rules, we derived three types of objects, twenty-nine methods, and four levels of strictness. The following sections describe them in detail.
Validation
The sufficiency of the objects and methods derived from 23 RFPs as elements required to develop design rules for RPFs for buildings were validated by analyzing design guidelines described in four additional RFPs using the objects and methods and checking whether design guidelines existed that could not be expressed using the derived objects and methods. The four RFPs were for a residential complex, an exhibition facility, an office building, and a hospital. The four RFPs were randomly selected
Applicability test
The objects and methods derived from the RFP analysis were implemented in a new BIM model checker, ‘abimo’ [48]. We specified a set of sample rules in SWRL using the objects and methods derived from the analysis and translated the SWRL rules to Python scripts using the SWRL-to-Python translator that we developed because ‘abimo’ uses Python as a rule description language. Translated rules were tested for the reliability and validity.
Fig. 7 illustrates an example of testing the ‘getElementWidth’
Discussion and conclusion
This study reported the analysis results of RFPs for buildings for deriving the objects and methods required to develop computer-interpretable rules for checking the compliance of RFPs in building design. RFP has received little attention in the automated rule-based BIM model checking field. However, many previous studies pointed out the inefficiency and unreliability of manual checking of the compliance of a design with an RFP and the efficiency of BIM-based automated design checking.
This
Acknowledgements
This research was supported by a grant (14AUDP-C067817-02) from the Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Republic of Korea.
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